SoroushVahidi/scidocs huggingface dataset:
Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera
(2012)

2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping.
Architecting the next generation of service-based SCADA/DCS system of systems
(2011)

SCADA and DCS systems are in the heart of the modern industrial infrastructure. The rapid changes in the networked embedded systems and the way industrial applications are designed and implemented, call for a shift in the architectural paradigm. Next generation SCADA and DCS systems will be able to foster cross-layer collaboration with the shop-floor devices as well as in-network and enterprise applications. Ecosystems driven by (web) service based interactions will enable stronger coupling of real-world and the business side, leading to a new generation of monitoring and control applications and services witnessed as the integration of large-scale systems of systems that are constantly evolving to address new user needs.
Information Processing in Medical Imaging
(2017)

In this paper, we present novel algorithms to compute robust statistics from manifold-valued data. Specifically, we present algorithms for estimating the robust Fréchet Mean (FM) and performing a robust exact-principal geodesic analysis (ePGA) for data lying on known Riemannian manifolds. We formulate the minimization problems involved in both these problems using the minimum distance estimator called the L2E. This leads to a nonlinear optimization which is solved efficiently using a Riemannian accelerated gradient descent technique. We present competitive performance results of our algorithms applied to synthetic data with outliers, the corpus callosum shapes extracted from OASIS MRI database, and diffusion MRI scans from movement disorder patients respectively.
Complex Embeddings for Simple Link Prediction
(2016)

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.1
SQuAD: 100, 000+ Questions for Machine Comprehension of Text
(2016)

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.
Freebase: A Shared Database of Structured General Human Knowledge
(2007)

Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
(2017)

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.1
Emotion recognition with multimodal features and temporal models
(2017)

This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.
Recommendation to Groups
(2007)

Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.
The cyber threat landscape: Challenges and future research directions
(2011)

Cyber threats are becoming more sophisticated with the blending of once distinct types of attack into more damaging forms. Increased variety and volume of attacks is inevitable given the desire of financially and criminally-motivated actors to obtain personal and confidential information, as highlighted in this paper. We describe how the Routine Activity Theory can be applied to mitigate these risks by reducing the opportunities for cyber crime to occur, making cyber crime more difficult to commit and by increasing the risks of detection and punishment associated with committing cyber crime. Potential research questions are also identified. a 2011 Elsevier Ltd. All rights reserved.
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study
(2010)

The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.
A connectionist approach to dynamic resource management for virtualised network functions
(2016)

Network Functions Virtualisation (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating Network Functions (NFs) from traditional middleboxes, NFV is expected to lead to reduced CAPEX and OPEX, and to more agile services. However, one of the main challenges to achieving these objectives is on how physical resources can be efficiently, autonomously, and dynamically allocated to Virtualised Network Functions (VNFs) whose resource requirements ebb and flow. In this paper, we propose a Graph Neural Network (GNN)-based algorithm which exploits Virtual Network Function Forwarding Graph (VNF-FG) topology information to predict future resource requirements for each Virtual Network Function Component (VNFC). The topology information of each VNFC is derived from combining its past resource utilisation as well as the modelled effect on the same from VNFCs in its neighbourhood. Our proposal has been evaluated using a deployment of a virtualised IP Multimedia Subsystem (IMS), and real VoIP traffic traces, with results showing an average prediction accuracy of 90%. Moreover, compared to a scenario where resources are allocated manually and/or statically, our proposal reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.
A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$  Noise Corner and an AC-Coupled Ripple Reduction Loop
(2009)

This paper presents a chopper instrumentation amplifier for interfacing precision thermistor bridges. For high CMRR and DC gain, the amplifier employs a three-stage current-feedback topology with nested-Miller compensation. By chopping both the input and intermediate stages of the amplifier, a 1 mHz 1/f noise corner was achieved at an input-referred noise power spectral density (PSD) of 15 nV/¿Hz. To reduce chopper ripple, the amplifier employs a continuous-time AC-coupled ripple reduction loop. Due to its continuous-time nature, the loop causes no noise folding to DC and hence offers improved noise performance over auto-zeroed amplifiers. The loop reduces chopper ripple by more than 60 dB, to levels below the amplifier's own input-referred noise. Furthermore, a maximum input referred offset of 5 ¿ V and a CMRR greater than 120 dB were measured at a supply current of 230 ¿A at 5 V.
Digitalism: The New Realism?
(2014)

Today’s society is increasingly digitalised, with mobile smartphones being routinely carried and used by a significant percentage of the population. This provides an augmented experience for the individual that does not depend on their geographical separation with respect to their community of friends and other contacts. This changes the nature of relationships between people. Individuals may live in a “digital bubble”, close to others physically, but far away from them in their digital world. More specifically, digital images can be generated and shared with ever greater ease. Sometimes the digital image takes on an important part of the individual’s experience of reality. This paper explores examples of the phenomenon, within the context of the arts in particular and culture in general. We also consider the assortment of terms used in a variety of ways by researchers in different fields with regard to our ever more digital society, such as digitalism, digitality, digitalisation, digital culture, digital philosophy, etc. We survey these terms, exploring them from alternative viewpoints, including sociological and philosophical aspects, and attempt to pinpoint some of these terms more precisely, especially in a cultural and artistic context.
Strategic Business / IT Alignment using Goal Models
(2008)

Since few years, enterprise information technologies (IT) are no more seen as a simple technological support for business strategies in the enterprise. Moreover, standalone IT departments are created in order to support evolution and growth of IT in the enterprise. Often, IT department defines a specific strategy describing vision, goals and objectives of IT development in the organization. However, to remain competitive, IT strategy and IT investment should be coherent with global enterprise strategies. The continuous process of preserving coherence between Business/IT strategies is widely known as strategic Business/IT alignment. Our work aims at discussing the relation between interview based Business IT alignement discovery and the Strategic Alignement Model proposed by Venkatraman. The paper proposes also modeling tools and engineering methodologies to support this alignment process.
On the use of X-vectors for Robust Speaker Recognition
(2018)

Text-independent speaker verification (SV) is currently in the process of embracing DNN modeling in every stage of SV system. Slowly, the DNN-based approaches such as end-to-end modelling and systems based on DNN embeddings start to be competitive even in challenging and diverse channel conditions of recent NIST SREs. Domain adaptation and the need for a large amount of training data are still a challenge for current discriminative systems and (unlike with generative models), we see significant gains from data augmentation, simulation and other techniques designed to overcome lack of training data. We present an analysis of a SV system based on DNN embeddings (x-vectors) and focus on robustness across diverse data domains such as standard telephone and microphone conversations, both in clean, noisy and reverberant environments. We also evaluate the system on challenging far-field data created by re-transmitting a subset of NIST SRE 2008 and 2010 microphone interviews. We compare our results with the stateof-the-art i-vector system. In general, we were able to achieve better performance with the DNN-based systems, but most importantly, we have confirmed the robustness of such systems across multiple data domains.
User experiences and expectations of vibrotactile, thermal and squeeze feedback in interpersonal communication
(2012)

Katja Suhonen Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland katja.suhonen@tut.fi Kaisa Väänänen-Vainio-Mattila Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland kaisa.vaananen-vainiomattila@tut.fi Kalle Mäkelä Tampere University of Technology, Department of Electronics, P.O.Box 692, 33101 Tampere, Finland kalle.makela@tut.fi
Differences in kinematics and electromyographic activity between men and women during the single-legged squat.
(2003)

BACKGROUND
Numerous factors have been identified as potentially increasing the risk of anterior cruciate ligament injury in the female athlete. However, differences between the sexes in lower extremity coordination, particularly hip control, are only minimally understood.


HYPOTHESIS
There is no difference in kinematic or electromyographic data during the single-legged squat between men and women.


STUDY DESIGN
Descriptive comparison study.


METHODS
We kinematically and electromyographically analyzed the single-legged squat in 18 intercollegiate athletes (9 male, 9 female). Subjects performed five single-legged squats on their dominant leg, lowering themselves as far as possible and then returning to a standing position without losing balance.


RESULTS
Women demonstrated significantly more ankle dorsiflexion, ankle pronation, hip adduction, hip flexion, hip external rotation, and less trunk lateral flexion than men. These factors were associated with a decreased ability of the women to maintain a varus knee position during the squat as compared with the men. Analysis of all eight tested muscles demonstrated that women had greater muscle activation compared with men. When each muscle was analyzed separately, the rectus femoris muscle activation was found to be statistically greater in women in both the area under the linear envelope and maximal activation data.


CONCLUSIONS
Under a physiologic load in a position commonly assumed in sports, women tend to position their entire lower extremity and activate muscles in a manner that could increase strain on the anterior cruciate ligament.
Procedural Content Generation: Goals, Challenges and Actionable Steps
(2013)

This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multi-content PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately. 1998 ACM Subject Classification I.2.1 Applications and Expert Systems: Games
Time-to-Digital Converter Using a Tuned-Delay Line Evaluated in 28-, 40-, and 45-nm FPGAs
(2016)

This paper proposes a bin-width tuning method for a field-programmable gate array (FPGA)-based delay line for a time-to-digital converter (TDC). Changing the hit transitions and sampling patterns of the carry chain considering delays of the sum and carry-out bins can improve the bin-width uniformity and thus measurement precision. The proposed sampling method was evaluated and compared with the ordinary tapped-delay-line (TDL) method in three different types of FPGAs: Kintex-7, Virtex-6, and Spartan-6. The linearity, equivalent bin width, and measurement precision improved for all the evaluated FPGAs by adopting the proposed method. The measurement precision obtained using the simple TDL architecture is comparable with other complex TDC architectures. In addition, the proposed method improves bin-width uniformity and measurement precision while maintaining the advantages of TDL TDCs, that is, fast conversion rate and small resource usage. Furthermore, the enhanced linearity of the delay line can also improve other carry-chain-based FPGA-TDCs.
Control structure design for complete chemical plants
(2004)

Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ‘‘complexity number’’ for the control system. # 2003 Elsevier Ltd. All rights reserved.
Reducing Uncertainty of Low-Sampling-Rate Trajectories
(2012)

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.
Towards reliable traffic sign recognition
(2009)

The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS [1]. In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.
3-Dimensional Analysis on the GIDL Current of Body-tied Triple Gate FinFET
(2006)

Triple gate FinFET is emerging as a promising candidate for the future CMOS device structures because of its immunity to short-channel effect. However, the suppression of GIDL is a significant challenge for its application. In this paper, we discuss the characteristics of GIDL on FinFET and extensively analyze the influence of the device technology on GIDL. The analysis is expected to give guidelines to the future development of triple gate FinFET